A new simultaneous experimental and theoretical interdisciplinary approach to study information processing in neuronal networks is suggested. It combines a detailed electro-morphological study at the cellular level with mathematical analysis using a new tool for the Simulation of Neuronal Networks (SONN) which we plan to develop. The idea is to consider neurons as a composition of subunits (e.g., axonal branch point), each performs specific information processing functions (Parnas and Segev, 1979). These functional subunits are first characterized experimentally, then analyzed using detailed partial differential equations (P.D.E) models (e.g., Parnas et al., 1986 a, b, c). Investigating these experimentally based models will eventually allow to abstract the subunits as independent processors or State Machines (S.M), which transform input to output and changes their internal states. SONN will include both P.D.E and the S.M models, the latter will serve to build a whole neuronal network and to analyze its information processing aspects in comparison to the experimental network. We believe that this approach will help to establish a theoretical framework for connecting biophysical mechanisms to the function of the neural-network and will aid to refine and redefine models in Artificial Intelligence. The heart ganglion of the lobster is the experimental network we plan to investigate. The initial modeling will be done with SPICE, a general-purpose electrical network analysis program, which we have adapted for the use of neurobiologists (Segev et al., 1985). However, its limitations and the realization that an appropriate modeling tool is crucial for our purposes, prompted us to propose to develop SONN for performing "on line" theoretical experiments. The emphasis is on developing graphical and mathematical tools to visualize the morphology and the electrical behavior of the modeled system using color codes. We believe this approach will lead to a better understanding of the principles that underlie the function of neuronal networks.